



'''simple docstring'''


from collections import defaultdict







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   Optional[Any]    ):
						lowercase   =				1
						lowercase   =				True
						for v in tree[start]:
												if v not in visited:
																		ret += dfs(UpperCamelCase_    )
						if ret % 2 == 0:
												cuts.append(UpperCamelCase_    )
						return ret







def   SCREAMING_SNAKE_CASE					(					):
						dfs(1    )


if __name__ == "__main__":
						lowercase_       ,							lowercase_   :					Dict        =     10, 9
						lowercase_   :					Optional[Any]        =     defaultdict(list)
						lowercase_   :					Tuple        =     {}
						lowercase_   :					Optional[int]        =     []
						lowercase_   :					List[Any]        =     0
						lowercase_   :					List[Any]        =     [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
						for u, v in edges:
												tree[u].append(v)
												tree[v].append(u)
						even_tree()
						print(len(cuts) - 1)



import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
     import jax
@require_flax
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				FlaxAutoencoderKL







   @property
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      4
    UpperCamelCase			  =      3
    UpperCamelCase			  =      (32, 32)

    UpperCamelCase			  =      jax.random.PRNGKey(0				)
    UpperCamelCase			  =      jax.random.uniform(lowerCamelCase_   ,   ((batch_size, num_channels) + sizes)				)

    return {"sample": image, "prng_key": prng_key}







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      {
        """block_out_channels""": [32, 64],
        """in_channels""": 3,
        """out_channels""": 3,
        """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
        """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
        """latent_channels""": 4,
    }
    UpperCamelCase			  =      self.dummy_input
    return init_dict, inputs_dict
